Automatic Cardiac Arrhythmia Classification Using Residual Network Combined With Long Short-Term Memory

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 17
• Main Authors: Kim, Yun Kwan, Lee, Minji, Song, Hee Seok, Lee, Seong-Whan
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrhythmia; Arrhythmia classification; Atrial fibrillation; augmentation; Cardiac arrhythmia; Cardiology; Classification; Deep learning; Diagnosis; Electrocardiography; electrocardiography (ECG); Feature extraction; few shot; Fibrillation; long short-term memory; Machine learning; Picture archiving and communication systems; residual network (ResNet); Rhythm; squeeze-and-excitation (SE) block

Diagnosis and classification of arrhythmia, which is associated with abnormal electrical activities in the heart, are critical for clinical treatments. Previous studies focused on the diagnosis of atrial fibrillation, which is the most common arrhythmia in adults. The classification performance achi...